Given the hypercorrelation processed by pyramidal aggregation, we propose to additionally utilize the appearance embedding obtained from query feature maps to effectively decode the hypercorrelation to a query mask. From the perspective of correlation maps, this setting can help finding more accurate correspondence as the appearance affinity information helps to filter out the erroneous matching scores, as proven in stereo matching literature, e.g., Cost Volume Filtering (CVF) [1]}, [2]}. Note that HSNet [3]} only decodes the matching costs with a series of 2D convolutions, thus often suffering from ambiguities in the matching costs.
